OpenRaiser/Intern-Atlas huggingface dataset:
Two-View Fine-grained Classification of Plant Species
Venue: arXiv cs.CV 2021
Year: 2021

Automatic plant classification is a challenging problem due to the wide biodiversity of the existing plant species in a fine-grained scenario. Powerful deep learning architectures have been used to improve the classification performance in such a fine-grained problem, but usually building models that are highly dependent on a large training dataset and which are not scalable. In this paper, we propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. It uses the botanical taxonomy as a basis for a coarse-to-fine strategy applied to identify the plant genus and species. The two-view representation provides complementary global and local features of leaf images. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species. The experimental results on two challenging fine-grained datasets of leaf images (i.e. LifeCLEF 2015 and LeafSnap) have shown the effectiveness of the proposed method, which achieved recognition accuracy of 0.87 and 0.96 respectively.
Coopetition Against an Amazon
Venue: arXiv cs.GT 2021
Year: 2021

This paper studies cooperative data-sharing between competitors vying to predict a consumer's tastes. We design optimal data-sharing schemes both for when they compete only with each other, and for when they additionally compete with an Amazon -- a company with more, better data. We show that simple schemes -- threshold rules that probabilistically induce either full data-sharing between competitors, or the full transfer of data from one competitor to another -- are either optimal or approximately optimal, depending on properties of the information structure. We also provide conditions under which firms share more data when they face stronger outside competition, and describe situations in which this conclusion is reversed.
A Way Around UMIP and Descriptor-Table Exiting via TSX-based Side-Channel
Venue: arXiv cs.CR 2021
Year: 2021

Nowadays, in operating systems, numerous protection mechanisms prevent or limit the user-mode applicationsto access the kernels internal information. This is regularlycarried out by software-based defenses such as Address Space Layout Randomization (ASLR) and Kernel ASLR(KASLR). They play pronounced roles when the security of sandboxed applications such as Web-browser are considered.Armed with arbitrary write access in the kernel memory, if these protections are bypassed, an adversary could find a suitable where to write in order to get an elevation of privilege or code execution in ring 0. In this paper, we introduce a reliable method based on Transactional Synchronization Extensions (TSX) side-channel leakage to reveal the address of the Global Descriptor Table (GDT) and Interrupt Descriptor Table (IDT). We indicate that by detecting these addresses, one could execute instructions to sidestep the Intels User-Mode InstructionPrevention (UMIP) and the Hypervisor-based mitigation and, consequently, neutralized them. The introduced method is successfully performed after the most recent patches for Meltdown and Spectre. Moreover, the implementation of the proposed approach on different platforms, including the latest releases of Microsoft Windows, Linux, and, Mac OSX with the latest 9th generation of Intel processors, shows that the proposed mechanism is independent from the Operating System implementation. We demonstrate that a combinationof this method with call-gate mechanism (available in modernprocessors) in a chain of events will eventually lead toa system compromise despite the limitations of a super-secure sandboxed environment in the presence of Windows proprietary Virtualization Based Security (VBS). Finally, we suggest the software-based mitigation to avoid these issues with an acceptable overhead cost.
VideoForensicsHQ: Detecting High-quality Manipulated Face Videos
Venue: arXiv cs.CV 2021
Year: 2021

There are concerns that new approaches to the synthesis of high quality face videos may be misused to manipulate videos with malicious intent. The research community therefore developed methods for the detection of modified footage and assembled benchmark datasets for this task. In this paper, we examine how the performance of forgery detectors depends on the presence of artefacts that the human eye can see. We introduce a new benchmark dataset for face video forgery detection, of unprecedented quality. It allows us to demonstrate that existing detection techniques have difficulties detecting fakes that reliably fool the human eye. We thus introduce a new family of detectors that examine combinations of spatial and temporal features and outperform existing approaches both in terms of detection accuracy and generalization.
Why are many businesses instilling a DevOps culture into their organization?
Venue: arXiv cs.SE 2021
Year: 2021

Context: DevOps can be defined as a cultural movement to improve and accelerate the delivery of business value by making the collaboration between development and operations effective. Although this movement is relatively recent, there exist an intensive research around DevOps. However, the real reasons why companies move to DevOps and the results they expect to obtain have been paid little attention in real contexts. Objective: This paper aims to help practitioners and researchers to better understand the context and the problems that many companies face day to day in their organizations when they try to accelerate software delivery and the main drivers that move these companies to adopting DevOps. Method: We conducted an exploratory study by leveraging in depth, semi-structured interviews to relevant stakeholders of 30 multinational software-intensive companies, together industrial workshops and observations at organizations' facilities that supported triangulation. Additionally, we conducted an inter-coder agreement analysis, which is not usually addressed in qualitative studies in software engineering, to increase reliability and reduce authors bias of the drawn findings. Results: The research explores the problems and expected outcomes that moved companies to adopt DevOps and reveals a set of patterns and anti-patterns about the reasons why companies are instilling a DevOps culture. Conclusions: This study aims to strengthen evidence and support practitioners in making better informed about which problems trigger a DevOps transition and most common expected results.
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack
Venue: arXiv cs.CV 2021
Year: 2021

Deep neural networks have achieved substantial achievements in several computer vision areas, but have vulnerabilities that are often fooled by adversarial examples that are not recognized by humans. This is an important issue for security or medical applications. In this paper, we propose an ensemble model training framework with random layer sampling to improve the robustness of deep neural networks. In the proposed training framework, we generate various sampled model through the random layer sampling and update the weight of the sampled model. After the ensemble models are trained, it can hide the gradient efficiently and avoid the gradient-based attack by the random layer sampling method. To evaluate our proposed method, comprehensive and comparative experiments have been conducted on three datasets. Experimental results show that the proposed method improves the adversarial robustness.
Concurrency-related Flaky Test Detection in Android apps
Venue: arXiv cs.SE 2021
Year: 2021

Validation of Android apps via testing is difficult owing to the presence of flaky tests. Due to non-deterministic execution environments, a sequence of events (a test) may lead to success or failure in unpredictable ways. In this work, we present an approach and tool FlakeShovel for detecting flaky tests through systematic exploration of event orders. Our key observation is that for a test in a mobile app, there is a testing framework thread which creates the test events, a main User-Interface (UI) thread processing these events, and there may be several other background threads running asynchronously. For any event e whose execution involves potential non-determinism, we localize the earliest (latest) event after (before) which e must happen.We then efficiently explore the schedules between the upper/lower bound events while grouping events within a single statement, to find whether the test outcome is flaky. We also create a suite of subject programs called DroidFlaker to study flaky tests in Android apps. Our experiments on subject-suite DroidFlaker demonstrate the efficacy of our flaky test detection. Our work is complementary to existing flaky test detection tools like Deflaker which check only failing tests. FlakeShovel can detect flaky tests among passing tests, as shown by our approach and experiments.
Intent Mining from past conversations for conversational agent
Venue: arXiv cs.CL 2021
Year: 2021

Conversational systems are of primary interest in the AI community. Chatbots are increasingly being deployed to provide round-the-clock support and to increase customer engagement. Many of the commercial bot building frameworks follow a standard approach that requires one to build and train an intent model to recognize a user input. Intent models are trained in a supervised setting with a collection of textual utterance and intent label pairs. Gathering a substantial and wide coverage of training data for different intent is a bottleneck in the bot building process. Moreover, the cost of labeling a hundred to thousands of conversations with intent is a time consuming and laborious job. In this paper, we present an intent discovery framework that involves 4 primary steps: Extraction of textual utterances from a conversation using a pre-trained domain agnostic Dialog Act Classifier (Data Extraction), automatic clustering of similar user utterances (Clustering), manual annotation of clusters with an intent label (Labeling) and propagation of intent labels to the utterances from the previous step, which are not mapped to any cluster (Label Propagation); to generate intent training data from raw conversations. We have introduced a novel density-based clustering algorithm ITER-DBSCAN for unbalanced data clustering. Subject Matter Expert (Annotators with domain expertise) manually looks into the clustered user utterances and provides an intent label for discovery. We conducted user studies to validate the effectiveness of the trained intent model generated in terms of coverage of intents, accuracy and time saving concerning manual annotation. Although the system is developed for building an intent model for the conversational system, this framework can also be used for a short text clustering or as a labeling framework.
On the impact of treewidth in the computational complexity of freezing dynamics
Venue: arXiv cs.DM 2021
Year: 2021

An automata network is a network of entities, each holding a state from a finite set and evolving according to a local update rule which depends only on its neighbors in the network's graph. It is freezing if there is an order on states such that the state evolution of any node is non-decreasing in any orbit. They are commonly used to model epidemic propagation, diffusion phenomena like bootstrap percolation or cristal growth. In this paper we establish how treewidth and maximum degree of the underlying graph are key parameters which influence the overall computational complexity of finite freezing automata networks. First, we define a general model checking formalism that captures many classical decision problems: prediction, nilpotency, predecessor, asynchronous reachability. Then, on one hand, we present an efficient parallel algorithm that solves the general model checking problem in NC for any graph with bounded degree and bounded treewidth. On the other hand, we show that these problems are hard in their respective classes when restricted to families of graph with polynomially growing treewidth. For prediction, predecessor and asynchronous reachability, we establish the hardness result with a fixed set-defiend update rule that is universally hard on any input graph of such families.
Temporal Network Motifs: Models, Limitations, Evaluation
Venue: arXiv cs.SI 2021
Year: 2021

Investigating the frequency and distribution of small subgraphs with a few nodes/edges, i.e., motifs, is an effective analysis method for static networks. Motif-driven analysis is also useful for temporal networks where the spectrum of motifs is significantly larger due to the additional temporal information on edges. This variety makes it challenging to design a temporal motif model that can consider all aspects of temporality. In the literature, previous works have introduced various models that handle different characteristics. In this work, we compare the existing temporal motif models and evaluate the facets of temporal networks that are overlooked in the literature. We first survey four temporal motif models and highlight their differences. Then, we evaluate the advantages and limitations of these models with respect to the temporal inducedness and timing constraints. In addition, we suggest a new lens, event pairs, to investigate temporal correlations. We believe that our comparative survey and extensive evaluation will catalyze the research on temporal network motif models.
Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text
Venue: arXiv cs.CL 2021
Year: 2021

Recent advances in machine learning have led to computer systems that are human-like in behaviour. Sentiment analysis, the automatic determination of emotions in text, is allowing us to capitalize on substantial previously unattainable opportunities in commerce, public health, government policy, social sciences, and art. Further, analysis of emotions in text, from news to social media posts, is improving our understanding of not just how people convey emotions through language but also how emotions shape our behaviour. This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications. We also discuss discuss how, without careful fore-thought, sentiment analysis has the potential for harmful outcomes. We outline the latest lines of research in pursuit of fairness in sentiment analysis.
HiVision: Rapid Visualization of Large-Scale Spatial Vector Data
Venue: arXiv cs.GR 2021
Year: 2021

Rapid visualization of large-scale spatial vector data is a long-standing challenge in Geographic Information Science. In existing methods, the computation overheads grow rapidly with data volumes, leading to the incapability of providing real-time visualization for large-scale spatial vector data, even with parallel acceleration technologies. To fill the gap, we present HiVision, a display-driven visualization model for large-scale spatial vector data. Different from traditional data-driven methods, the computing units in HiVision are pixels rather than spatial objects to achieve real-time performance, and efficient spatial-index-based strategies are introduced to estimate the topological relationships between pixels and spatial objects. HiVision can maintain exceedingly good performance regardless of the data volume due to the stable pixel number for display. In addition, an optimized parallel computing architecture is proposed in HiVision to ensure the ability of real-time visualization. Experiments show that our approach outperforms traditional methods in rendering speed and visual effects while dealing with large-scale spatial vector data, and can provide interactive visualization of datasets with billion-scale points/segments/edges in real-time with flexible rendering styles. The HiVision code is open-sourced at https://github.com/MemoryMmy/HiVision with an online demonstration.
Symbolic and Structural Model-Checking
Venue: arXiv cs.DC 2021
Year: 2021

Brute-force model-checking consists in exhaustive exploration of the state-space of a Petri net, and meets the dreaded state-space explosion problem. In contrast, this paper shows how to solve model-checking problems using a combination of techniques that stay in complexity proportional to the size of the net structure rather than to the state-space size. We combine an SMT based over-approximation to prove that some behaviors are unfeasible, an under-approximation using memory-less sampling of runs to find witness traces or counter-examples, and a set of structural reduction rules that can simplify both the system and the property. This approach was able to win by a clear margin the model-checking contest 2020 for reachability queries as well as deadlock detection, thus demonstrating the practical effectiveness and general applicability of the system of rules presented in this paper.
Fourier Neural Networks as Function Approximators and Differential Equation Solvers
Venue: arXiv cs.LG 2021
Year: 2021

We present a Fourier neural network (FNN) that can be mapped directly to the Fourier decomposition. The choice of activation and loss function yields results that replicate a Fourier series expansion closely while preserving a straightforward architecture with a single hidden layer. The simplicity of this network architecture facilitates the integration with any other higher-complexity networks, at a data pre- or postprocessing stage. We validate this FNN on naturally periodic smooth functions and on piecewise continuous periodic functions. We showcase the use of this FNN for modeling or solving partial differential equations with periodic boundary conditions. The main advantages of the current approach are the validity of the solution outside the training region, interpretability of the trained model, and simplicity of use.
Spatial organisation of French research from the scholarly publication standpoint (1999-2017): Long-standing dynamics and policy-induced disorder
Venue: arXiv cs.DL 2021
Year: 2021

In social processes, long-term trends can be influenced or disrupted by various factors, including public policy. When public policies depend on a misrepresentation of trends in the areas they are aimed at, they become random and disruptive, which can be interpreted as a source of disorder. Here we consider policies on the spatial organization of the French Higher Education and Research system, which reflects the authorities' hypothesis that scientific excellence is the prerogative of a few large urban agglomerations. By geographically identifying all the French publications listed in the Web of Science databases between 1999 and 2017, we highlight a spatial deconcentration trend, which has slowed down in recent years due to a freezed growth of the teaching force. This deconcentration continues, however, to sustain the growth of scientific production in small and medium-sized towns. An examination of the large conurbations shows the relative decline of sites that nevertheless have been highlighted as examples to be followed by the Excellence policies (Strasbourg among others). The number of students and faculty has grown less there, and it is a plaussible explanation for the relative decline in scientific production. We show that the publication output of a given site depends directly and strongly on the number of researchers hosted there. Based on precise data at the French level, our results confirm what is already known at world scale. In conclusion, we question the amount of disorder resulting from policies aligned with poorly assessed trends.
Korali: Efficient and Scalable Software Framework for Bayesian Uncertainty Quantification and Stochastic Optimization
Venue: arXiv cs.DC 2021
Year: 2021

We present Korali, an open-source framework for large-scale Bayesian uncertainty quantification and stochastic optimization. The framework relies on non-intrusive sampling of complex multiphysics models and enables their exploitation for optimization and decision-making. In addition, its distributed sampling engine makes efficient use of massively-parallel architectures while introducing novel fault tolerance and load balancing mechanisms. We demonstrate these features by interfacing Korali with existing high-performance software such as Aphros, Lammps (CPU-based), and Mirheo (GPU-based) and show efficient scaling for up to 512 nodes of the CSCS Piz Daint supercomputer. Finally, we present benchmarks demonstrating that Korali outperforms related state-of-the-art software frameworks.
CedrusDB: Persistent Key-Value Store with Memory-Mapped Lazy-Trie
Venue: arXiv cs.DB 2021
Year: 2021

As a result of RAM becoming cheaper, there has been a trend in key-value store design towards maintaining a fast in-memory index (such as a hash table) while logging user operations to disk, allowing high performance under failure-free conditions while still being able to recover from failures. This design, however, comes at the cost of long recovery times or expensive checkpoint operations. This paper presents a new in-memory index that is also storage-friendly. A "lazy-trie" is a variant of the hash-trie data structure that achieves near-optimal height, has practical storage overhead, and can be maintained on-disk with standard write-ahead logging. We implemented CedrusDB, persistent key-value store based on a lazy-trie. The lazy-trie is kept on disk while made available in memory using standard memory-mapping. The lazy-trie organization in virtual memory allows CedrusDB to better leverage concurrent processing than other on-disk index schemes (LSMs, B+-trees). CedrusDB achieves comparable or superior performance to recent log-based in-memory key-value stores in mixed workloads while being able to recover quickly from failures.
Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction
Venue: arXiv cs.CV 2021
Year: 2021

Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models. In order to gain a better understanding of the complexity of trajectory prediction tasks and following the intuition, that more complex datasets contain more information, an approach for quantifying the amount of information contained in a dataset from a prototype-based dataset representation is proposed. The dataset representation is obtained by first employing a non-trivial spatial sequence alignment, which enables a subsequent learning vector quantization (LVQ) stage. A large-scale complexity analysis is conducted on several human trajectory prediction benchmarking datasets, followed by a brief discussion on indications for human trajectory prediction and benchmarking.
A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers
Venue: arXiv cs.LG 2021
Year: 2021

Our research aims to propose a new performance-explainability analytical framework to assess and benchmark machine learning methods. The framework details a set of characteristics that systematize the performance-explainability assessment of existing machine learning methods. In order to illustrate the use of the framework, we apply it to benchmark the current state-of-the-art multivariate time series classifiers.